Abstract
In this paper, we present an LSTM approach to assess free short answers in tutorial dialogue contexts. A major advantage of the proposed method is that it does not require any sort of feature engineering. The method performs on par and even slightly better than existing state-of-the-art methods that rely on expert-engineered features.
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Acknowledgments
This research was partially sponsored by the University of Memphis and the Institute for Education Sciences under award R305A100875 to Dr. Vasile Rus.
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Maharjan, N., Gautam, D., Rus, V. (2018). Assessing Free Student Answers in Tutorial Dialogues Using LSTM Models. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_35
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DOI: https://doi.org/10.1007/978-3-319-93846-2_35
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